Combined fitness function based particle swarm optimization algorithm for system identification

Lu, Jianshan; Xie, Weidong; Zhou, Hongbo

doi:10.1016/j.cie.2016.03.007

Cited by 13 publications

(4 citation statements)

References 42 publications

(73 reference statements)

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“…Existing studies show that the efficiency of the PSO algorithm is influenced by the fitness function, and the selection of a suitable fitness function is beneficial to the performance of the algorithm (Lu et al, 2016). For hysteresis model parameter identification, the root mean square error (RMSE) is usually chosen as the fitness function of the PSO algorithm.…”

Section: Parameters Identificationmentioning

confidence: 99%

Modeling, identification, and high-speed compensation study of dynamic hysteresis nonlinearity for piezoelectric actuator

Zhou,

Dai,

Zhou

et al. 2024

Journal of Intelligent Material Systems and Structures

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Hysteresis nonlinearity widely exists in the piezoelectric actuator (PEA), and the hysteresis nonlinearity has strong dynamic characteristics that lead to deterioration of tracking performance. To decrease the positioning error caused by hysteresis nonlinearity, a generalized Bouc-Wen (GBW) hysteresis model and its compensation method are proposed in this paper. First, based on the Bouc-Wen hysteresis model, two asymmetric terms and a second-order IIR filter are applied to describe the asymmetric hysteresis and high-frequency phase lag characteristics of PEA. Then, the model parameters with strong relevance to frequency variation are modified as frequency-dependent parameters. Meanwhile, based on the particle swarm optimization (PSO) algorithm, a novel parameter identification algorithm is designed for identifying the parameters of GBW hysteresis model. Then, an inverse feedforward controller is constructed based on the GBW hysteresis model, and a composite compensation control algorithm combining PID controller and repetitive controller is developed to reduce the unmodeled dynamics errors and unknown external disturbances. Finally, the comparison experiment results show that the accuracy and performance of the GBW model proposed in this paper are much better than the classical Bouc-Wen (CBW) model and the enhanced Bouc-Wen (EBW) model, and the developed compensation controller has excellent control performance and robustness.

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Section: Parameters Identificationmentioning

confidence: 99%

Modeling, identification, and high-speed compensation study of dynamic hysteresis nonlinearity for piezoelectric actuator

Zhou,

Dai,

Zhou

et al. 2024

Journal of Intelligent Material Systems and Structures

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“…29 The particles start jumping from the initial positions through the given search space with velocities. 30 The best solution obtained from each particle is called personal best (Pbest), and the best solution obtained among all particles is called global best (Gbest).…”

Section: Introductionmentioning

confidence: 99%

“…Since its original development in 1995, PSO is a population‐based metaheuristic algorithm that imitates the social behavior of fish schools or bird flocks . The particles start jumping from the initial positions through the given search space with velocities . The best solution obtained from each particle is called personal best (Pbest), and the best solution obtained among all particles is called global best (Gbest).…”

Section: Introductionmentioning

confidence: 99%

A metaheuristic‐based approach to optimizing color design for military camouflage using particle swarm optimization

Lin

Prasetyo

2019

Color Research & Application

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Enhancing an existing military camouflage is an important component during the assessment of military camouflage. The current study proposed a new and practical approach to enhancing the undetectability of a military camouflage using particle swarm optimization (PSO). Eight different locations (20 × 50 pixels) in the one swamp background were selected to be the place of a human-shaped target. The PSO would generate newly proposed camouflage as an empirical parameter based on the lower and the upper bounds from selected four different colors in swamp background. The predictive algorithm was applied to adjust the optimum shift of %L*, %a*, and %b* from the original to the empirical parameter. Thirty participants were recruited to evaluate the original and newly proposed camouflages. Paired sample t test indicates that the newly proposed military camouflage had a significant lower camouflage similarity index value and a longer detection time. The PSO shows to be a method with good results; however, a comprehensive study using multiple backgrounds and patterns would be required to generalize the methodology to other background environments or camouflage patterns. K E Y W O R D Scamouflage similarity index, detection time, military camouflage, particle swarm optimization, target search

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“…In 1995, the foraging behavior of bird swarm inspired Kennedy and Eberhart to propose the particle swarm optimization (PSO) algorithm. PSO requires few parameter adjustments and is easy to implement; hence, it is the most commonly used swarm intelligence algorithm [11–20]. However, in practical applications, most problems are complicated design problems with multiple parameters, strong coupling, and nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Swarm Optimization Algorithm Based on Last-Eliminated Principle and Enhanced Information Sharing

Wang

Deng

et al. 2018

Computational Intelligence and Neuroscience

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In this study, an improved eliminate particle swarm optimization (IEPSO) is proposed on the basis of the last-eliminated principle to solve optimization problems in engineering design. During optimization, the IEPSO enhances information communication among populations and maintains population diversity to overcome the limitations of classical optimization algorithms in solving multiparameter, strong coupling, and nonlinear engineering optimization problems. These limitations include advanced convergence and the tendency to easily fall into local optimization. The parameters involved in the imported “local-global information sharing” term are analyzed, and the principle of parameter selection for performance is determined. The performances of the IEPSO and classical optimization algorithms are then tested by using multiple sets of classical functions to verify the global search performance of the IEPSO. The simulation test results and those of the improved classical optimization algorithms are compared and analyzed to verify the advanced performance of the IEPSO algorithm.

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Combined fitness function based particle swarm optimization algorithm for system identification

Cited by 13 publications

References 42 publications

Modeling, identification, and high-speed compensation study of dynamic hysteresis nonlinearity for piezoelectric actuator

Modeling, identification, and high-speed compensation study of dynamic hysteresis nonlinearity for piezoelectric actuator

A metaheuristic‐based approach to optimizing color design for military camouflage using particle swarm optimization

Improved Particle Swarm Optimization Algorithm Based on Last-Eliminated Principle and Enhanced Information Sharing

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